Diploma titles

Bayesian Machine Learning

• Nikita Balabin – Generative Prior Selection for Continual and Transfer Deep Learning
• Polina Proskura – Models Ensemble on Deep Neural Network Basis
• Roman Kail – M-Ensembles: Accuracy of Standard Ensembles at the Cost of (Almost) a Single Model
• Anna Nikolaeva – Improving Machine Learning Models with Monotonicity Constraints
• Oleg Alenkin – Bayesian Optimization of SHiP Elements
• Natalia Kozlovskaya – Imbalanced Classification Based on Ensembles of Decision Trees

Change point and Anomaly detection

• Evgeniya Romanenkova – Principled Change Point Detection for Semi-structured Data
• Viktoriia Snorovikhina – Unsupervised Anomaly Detection for Semi-structured Sequence Healthcare Data

Event sequences processing

• Vladislav Zhuzel – Processing of Event Sequences
• Mariya Kuzmina – Neural Architecture Search for the Problem of Daily News Text Classification
• Nina Kaploukhai – Adversarial Attacks and Methods of Protection for Sequential data
• Maria Begicheva – Embeddings for the Identification of the Bank Status
• Artem Zabolotnyi – Application of Deep Learning Methods for Time Series with Complex Structure
• Valerii Baianov – Aggregation of Heterogeneous Data in Deep Learning Models
• Pavel Shatalov – Speech Emotion Recognition
• Mark Zakharov – Analysis of Credit Risk Leading Indicators Based on Transaction Embeddings
• Rasul Khasianov – Anomaly Detection Using Embeddings for Sequential Healthcare Data
• Ivan Fursov – Adversarial Attacks on Symbolic Sequence Classifiers

Other

• Yunjeong Lee – Forecasting China’s Gas Consumption: Development of Simple and Strong Forecasting Model
• Vladimir Shenderov – Applicability of Drill Bit Grading Parameters for Real-time Rock Type Classification
• Nikita Bekezin – Interest Rate Elasticity of Demand for Bank Loans
• Ekaterina Orlova – Modern Approaches to Assessment of Demand Sensitivity Based on Uplift Models and Neural Networks